Statistical models for genetics data are often surprisingly challenging, and often requires advanced and new statistical methods. This project investigates a simple, widely used test (the TDT) for detecting association and/or linkage from parent child data. The transmission/disequilibrium test (TDT) has widespread use in statistical genetics, as it does not make any assumptions regarding mode of inheritance, penetrance rates, marker or disease allele frequencies or population stratification. It is understood to have high power for detecting association and/or linkage, and is very simple to apply. However, it does not directly estimate any of the underlying parameters in the allele transmission probability model, nor provide for follow-up estimation when the null model of no association or linkage as been rejected. This project is the first to estimate TDT model parameters, thereby solving a long-standing problem in statistical genetics. We introduce a new parametrization of the model, and show that the TDT has in fact rather low power over a broad range of the parameter space. Using a series of realistic simulations we show that our methods are essentially as powerful and robust as the classical TDT, while our confidence intervals for linkage and association provide significantly new useful information. Using a second new parameterization we introduce a likelihood ratio test and show that it is uniformaly more powerful than the TDT when testing for linkage. All results were thorougly tested using a series of realistic simulations and models, and demonstrated that our estimation methods are a valuable contribution to practical genetics data analysis.[1] Malley, Redner, Severini, Badner, Bailey-Wilson, Pajevic (2000). Estimation of Association and Linkage from Transmission/disequilibrium (TDT) Data. Under review at the American Journal of Human Genetics.